Sensor calibration is a key component for advanced driver-assistance systems. A number of the sensors found in the current generation of production vehicles are typically of low cost and as a consequence prone to time-varying offset and scale errors, and subject to large noise. For instance, the lateral acceleration and heading (yaw)-rate measurements can have drift and large noise in the sensor measurements, forcing the measurements to be reliable for prediction over a very limited time interval. Similarly, the sensor measuring the steering-wheel angle has an offset error that, when used for dead reckoning in a vehicle model, leads to prediction errors that accumulate over time. To complicate things even further, the wheel-speed sensors lead to errors in the vehicle-speed estimate due to a scale error in the tire radius estimate.
The recent surge for enabling new advanced driver-assistance systems (ADAS) and autonomous capabilities implies a need for sensor information that can be used over longer time intervals to reliably predict the vehicle motion. The underlying theme of how to achieve more reliable sensor information is to leverage sensor fusion, to utilize existing low-cost sensors as efficiently as possible for as many purposes and driver-assistance features as possible.
To that end, there is a need to determine offset and noise of the sensors of the vehicle. In addition, while some sensor calibration can be performed beforehand, when mounted in a vehicle, some sensors, such as an accelerometer, can have an effective noise level that differs from the a priori determined. The reason is that the sensor noise is dependent on a number of factors such as temperature, age, and where in the vehicle the sensor is placed. For instance, the higher the sensor is placed, the more of the disturbances from the suspension system affect the apparent noise in the sensor.
Accordingly, there is a need for a method and a system for real-time calibration of the offsets and the noise in the sensors of the vehicle. Unfortunately, known solutions are based on simplistic averaging techniques to compensate for the yaw rate and steering wheel bias. However, averaging methods are based on simplistic assumptions about the vehicle behavior, such as straight driving, and cannot estimate the offsets during general driving. See, e.g., a method described in U.S. Pat. No. 8,731,769.